@INPROCEEDINGS {chen2021icde,
author = { Chen, Xiaoshuang and Lai, Longbin and Qin, Lu and Lin, Xuemin and Liu, Boge },
booktitle = { 2021 IEEE 37th International Conference on Data Engineering (ICDE) },
title = {{ A Framework to Quantify Approximate Simulation on Graph Data }},
year = {2021},
volume = {},
ISSN = {},
pages = {1308-1319},
abstract = { Simulation and its variants (e.g., bisimulation and degree-preserving simulation) are useful in a wide spectrum of applications. However, all simulation variants are coarse "yes-or-no" indicators that simply confirm or refute whether one node simulates another, which limits the scope and power of their utility. Therefore, it is meaningful to develop a fractional χ-simulation measure to quantify the degree to which one node simulates another by the simulation variant χ. To this end, we first present several properties necessary for a fractional χ-simulation measure. Then, we present FSimχ, a general fractional χ-simulation computation framework that can be configured to quantify the extent of all χ-simulations. Comprehensive experiments and real-world case studies show the measure to be effective and the computation framework to be efficient. },
keywords = {Computational modeling;Conferences;Data engineering;Data models;Computational efficiency},
doi = {10.1109/ICDE51399.2021.00117},
url = {https://doi.ieeecomputersociety.org/10.1109/ICDE51399.2021.00117},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
month =apr
}